def _augment_batch(self, images, corners):
aug = iaa.SomeOf(
(0, None),
[
iaa.KeepSizeByResize(
iaa.Affine(
translate_px={"x": (10, 30)},
rotate=(-5, 5),
mode="edge",
fit_output=True,
)),
iaa.KeepSizeByResize(
iaa.Affine(shear=(-10, 10), mode="edge", fit_output=True)),
iaa.AddToHueAndSaturation((-50, 50)),
iaa.AverageBlur(k=(2, 5)),
],
random_order=True,
)
# Convert array of corners to list of KeypointsOnImage instances for use with the augmenter.
keypoints_from_corners = [
KeypointsOnImage(
[Keypoint(x=point[0], y=point[1]) for point in img_corners],
shape=self.dim,
) for img_corners in corners
]
images_augm, keypoints_augm = aug.augment(
images=images, keypoints=keypoints_from_corners)
# Convert augmented keypoints back to array of size (batch_size, 4, 2).
corners_augm = np.array(
[keypoints.to_xy_array() for keypoints in keypoints_augm])
return images_augm, corners_augm
def chapter_augmenters_keepsizebyresize():
# TODO add example images for heatmaps, segmaps
fn_start = "size/keepsizebyresize"
aug = iaa.KeepSizeByResize(iaa.Crop((20, 40), keep_size=False))
run_and_save_augseq(fn_start + "_crop.jpg",
aug,
[ia.quokka(size=(120, 120)) for _ in range(4 * 2)],
cols=4,
rows=2)
aug = iaa.KeepSizeByResize(iaa.Crop((20, 40), keep_size=False),
interpolation="nearest")
run_and_save_augseq(fn_start + "_crop_nearest.jpg",
aug,
[ia.quokka(size=(120, 120)) for _ in range(4 * 2)],
cols=4,
rows=2)
aug = iaa.KeepSizeByResize(
iaa.Crop((20, 40), keep_size=False),
interpolation=["nearest", "cubic"],
interpolation_heatmaps=iaa.KeepSizeByResize.SAME_AS_IMAGES,
interpolation_segmaps=iaa.KeepSizeByResize.NO_RESIZE)
run_and_save_augseq(fn_start + "_various_augmentables.jpg",
aug,
[ia.quokka(size=(120, 120)) for _ in range(4 * 4)],
cols=4,
rows=4)
def transform(self, in_data):
augmenter = iaa.Sequential([
iaa.LinearContrast(alpha=(0.8, 1.2)),
iaa.WithColorspace(
to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.Sequential([
# SV
iaa.WithChannels(
(1, 2),
iaa.Multiply(mul=(0.8, 1.2), per_channel=True),
),
# H
iaa.WithChannels(
(0, ),
iaa.Multiply(mul=(0.95, 1.05), per_channel=True),
),
]),
),
iaa.GaussianBlur(sigma=(0, 1.0)),
iaa.KeepSizeByResize(children=iaa.Resize((0.25, 1.0))),
])
augmenter = augmenter.to_deterministic()
for index in self._indices:
in_data[index] = augmenter.augment_image(in_data[index])
def YOLO():
"""
Data augmentation model for YOLOv3 training
"""
return iaa.Sequential([
iaa.KeepSizeByResize(
iaa.Affine(
scale=iap.Normal(1, 0.125),
translate_percent=0.1,
cval=128,
)),
iaa.Fliplr(0.5),
iaa.Resize({
"height": iap.Normal(1, 0.1),
"width": iap.Normal(1, 0.1)
}),
iaa.Resize({
"longer-side": 416,
"shorter-side": "keep-aspect-ratio"
}),
iaa.PadToFixedSize(416, 416, pad_cval=128),
iaa.MultiplyHueAndSaturation(mul_hue=iap.Uniform(0, 2),
mul_saturation=iap.Uniform(1 / 1.5, 1.5)),
iaa.AssertShape((None, 416, 416, 3)),
])
def get_fg_augmentation():
seq = iaa.Sequential([
iaa.Resize({
'height': 'keep',
'width': (0.9, 1.1)
}),
iaa.KeepSizeByResize(iaa.Rotate((-15, 15), fit_output=True))
])
return seq
def getImageAug():
seq = iaa.Sequential(
[
iaa.SomeOf(
(0, 2),
[
iaa.Identity(),
iaa.AverageBlur(k=((3, 5), (5, 7))),
iaa.Rotate((-45, 45)),
iaa.Affine(scale=(0.5, 0.95)),
iaa.Multiply((0.50, 1.1))
#,iaa.BlendAlphaRegularGrid(nb_rows=(4, 6), nb_cols=(1, 4),
# foreground=iaa.Multiply(0.0))
#,iaa.Cartoon()
,
iaa.Cutout(
nb_iterations=(1, 3), size=0.2, squared=False, cval=0),
iaa.Affine(shear=(-48, 48)),
iaa.Affine(translate_px={
"x": (-42, 42),
"y": (-36, 36)
}),
iaa.KeepSizeByResize(
iaa.Resize({
"height": (0.70, 0.90),
"width": (0.70, 0.90)
})),
iaa.CropAndPad(percent=(-0.2, 0.2))
#,iaa.PiecewiseAffine(scale=(0.01, 0.05))
,
iaa.PerspectiveTransform(scale=(0.01, 0.1))
#,iaa.WithPolarWarping(iaa.CropAndPad(percent=(-0.1, 0.1)))
#,iaa.ElasticTransformation(alpha=(0, 3.0), sigma=0.5)
])
#,iaa.SaveDebugImageEveryNBatches(folder_path, 100)
],
random_order=True)
return seq
def _augment_rgb(rgb):
augmenter = iaa.Sequential([
iaa.LinearContrast(alpha=(0.8, 1.2)),
iaa.WithColorspace(
to_colorspace="HSV",
from_colorspace="RGB",
children=iaa.Sequential([
# SV
iaa.WithChannels(
(1, 2),
iaa.Multiply(mul=(0.8, 1.2), per_channel=True),
),
# H
iaa.WithChannels(
(0, ),
iaa.Multiply(mul=(0.95, 1.05), per_channel=True),
),
]),
),
iaa.GaussianBlur(sigma=(0, 1.0)),
iaa.KeepSizeByResize(children=iaa.Resize((0.25, 1.0))),
])
return augmenter.augment_image(rgb)
seq = iaa.Sequential([
iaa.SomeOf((0, 2), [
iaa.Identity(),
iaa.AverageBlur(k=((3, 5), (5, 7))),
iaa.Rotate((-45, 45)),
iaa.Affine(scale=(0.5, 0.95)),
iaa.Multiply((0.50, 1.1)),
iaa.Cutout(nb_iterations=(1, 3), size=0.2, squared=False, cval=0),
iaa.Affine(shear=(-48, 48)),
iaa.Affine(translate_px={
"x": (-42, 42),
"y": (-36, 36)
}),
iaa.KeepSizeByResize(
iaa.Resize({
"height": (0.70, 0.90),
"width": (0.70, 0.90)
})),
iaa.CropAndPad(percent=(-0.2, 0.2)),
iaa.PerspectiveTransform(scale=(0.01, 0.1))
])
],
random_order=True)
def get_recent_model_dir(model_root_dir):
import os, glob
from pathlib import Path
model_root_dir = "models"
paths = sorted(Path(model_root_dir).iterdir(), key=os.path.getmtime)[::-1]
for p in paths:
def create_augmenters(height, width, height_augmentable, width_augmentable,
only_augmenters):
def lambda_func_images(images, random_state, parents, hooks):
return images
def lambda_func_heatmaps(heatmaps, random_state, parents, hooks):
return heatmaps
def lambda_func_keypoints(keypoints, random_state, parents, hooks):
return keypoints
def assertlambda_func_images(images, random_state, parents, hooks):
return True
def assertlambda_func_heatmaps(heatmaps, random_state, parents, hooks):
return True
def assertlambda_func_keypoints(keypoints, random_state, parents, hooks):
return True
augmenters_meta = [
iaa.Sequential([iaa.Noop(), iaa.Noop()],
random_order=False,
name="Sequential_2xNoop"),
iaa.Sequential([iaa.Noop(), iaa.Noop()],
random_order=True,
name="Sequential_2xNoop_random_order"),
iaa.SomeOf((1, 3),
[iaa.Noop(), iaa.Noop(), iaa.Noop()],
random_order=False,
name="SomeOf_3xNoop"),
iaa.SomeOf((1, 3),
[iaa.Noop(), iaa.Noop(), iaa.Noop()],
random_order=True,
name="SomeOf_3xNoop_random_order"),
iaa.OneOf([iaa.Noop(), iaa.Noop(), iaa.Noop()], name="OneOf_3xNoop"),
iaa.Sometimes(0.5, iaa.Noop(), name="Sometimes_Noop"),
iaa.WithChannels([1, 2], iaa.Noop(), name="WithChannels_1_and_2_Noop"),
iaa.Noop(name="Noop"),
iaa.Lambda(func_images=lambda_func_images,
func_heatmaps=lambda_func_heatmaps,
func_keypoints=lambda_func_keypoints,
name="Lambda"),
iaa.AssertLambda(func_images=assertlambda_func_images,
func_heatmaps=assertlambda_func_heatmaps,
func_keypoints=assertlambda_func_keypoints,
name="AssertLambda"),
iaa.AssertShape((None, height_augmentable, width_augmentable, None),
name="AssertShape"),
iaa.ChannelShuffle(0.5, name="ChannelShuffle")
]
augmenters_arithmetic = [
iaa.Add((-10, 10), name="Add"),
iaa.AddElementwise((-10, 10), name="AddElementwise"),
#iaa.AddElementwise((-500, 500), name="AddElementwise"),
iaa.AdditiveGaussianNoise(scale=(5, 10), name="AdditiveGaussianNoise"),
iaa.AdditiveLaplaceNoise(scale=(5, 10), name="AdditiveLaplaceNoise"),
iaa.AdditivePoissonNoise(lam=(1, 5), name="AdditivePoissonNoise"),
iaa.Multiply((0.5, 1.5), name="Multiply"),
iaa.MultiplyElementwise((0.5, 1.5), name="MultiplyElementwise"),
iaa.Dropout((0.01, 0.05), name="Dropout"),
iaa.CoarseDropout((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseDropout"),
iaa.ReplaceElementwise((0.01, 0.05), (0, 255),
name="ReplaceElementwise"),
#iaa.ReplaceElementwise((0.95, 0.99), (0, 255), name="ReplaceElementwise"),
iaa.SaltAndPepper((0.01, 0.05), name="SaltAndPepper"),
iaa.ImpulseNoise((0.01, 0.05), name="ImpulseNoise"),
iaa.CoarseSaltAndPepper((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseSaltAndPepper"),
iaa.Salt((0.01, 0.05), name="Salt"),
iaa.CoarseSalt((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseSalt"),
iaa.Pepper((0.01, 0.05), name="Pepper"),
iaa.CoarsePepper((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarsePepper"),
iaa.Invert(0.1, name="Invert"),
# ContrastNormalization
iaa.JpegCompression((50, 99), name="JpegCompression")
]
augmenters_blend = [
iaa.Alpha((0.01, 0.99), iaa.Noop(), name="Alpha"),
iaa.AlphaElementwise((0.01, 0.99), iaa.Noop(),
name="AlphaElementwise"),
iaa.SimplexNoiseAlpha(iaa.Noop(), name="SimplexNoiseAlpha"),
iaa.FrequencyNoiseAlpha((-2.0, 2.0),
iaa.Noop(),
name="FrequencyNoiseAlpha")
]
augmenters_blur = [
iaa.GaussianBlur(sigma=(1.0, 5.0), name="GaussianBlur"),
iaa.AverageBlur(k=(3, 11), name="AverageBlur"),
iaa.MedianBlur(k=(3, 11), name="MedianBlur"),
iaa.BilateralBlur(d=(3, 11), name="BilateralBlur"),
iaa.MotionBlur(k=(3, 11), name="MotionBlur")
]
augmenters_color = [
# InColorspace (deprecated)
iaa.WithColorspace(to_colorspace="HSV",
children=iaa.Noop(),
name="WithColorspace"),
iaa.WithHueAndSaturation(children=iaa.Noop(),
name="WithHueAndSaturation"),
iaa.MultiplyHueAndSaturation((0.8, 1.2),
name="MultiplyHueAndSaturation"),
iaa.MultiplyHue((-1.0, 1.0), name="MultiplyHue"),
iaa.MultiplySaturation((0.8, 1.2), name="MultiplySaturation"),
iaa.AddToHueAndSaturation((-10, 10), name="AddToHueAndSaturation"),
iaa.AddToHue((-10, 10), name="AddToHue"),
iaa.AddToSaturation((-10, 10), name="AddToSaturation"),
iaa.ChangeColorspace(to_colorspace="HSV", name="ChangeColorspace"),
iaa.Grayscale((0.01, 0.99), name="Grayscale"),
iaa.KMeansColorQuantization((2, 16), name="KMeansColorQuantization"),
iaa.UniformColorQuantization((2, 16), name="UniformColorQuantization")
]
augmenters_contrast = [
iaa.GammaContrast(gamma=(0.5, 2.0), name="GammaContrast"),
iaa.SigmoidContrast(gain=(5, 20),
cutoff=(0.25, 0.75),
name="SigmoidContrast"),
iaa.LogContrast(gain=(0.7, 1.0), name="LogContrast"),
iaa.LinearContrast((0.5, 1.5), name="LinearContrast"),
iaa.AllChannelsCLAHE(clip_limit=(2, 10),
tile_grid_size_px=(3, 11),
name="AllChannelsCLAHE"),
iaa.CLAHE(clip_limit=(2, 10),
tile_grid_size_px=(3, 11),
to_colorspace="HSV",
name="CLAHE"),
iaa.AllChannelsHistogramEqualization(
name="AllChannelsHistogramEqualization"),
iaa.HistogramEqualization(to_colorspace="HSV",
name="HistogramEqualization"),
]
augmenters_convolutional = [
iaa.Convolve(np.float32([[0, 0, 0], [0, 1, 0], [0, 0, 0]]),
name="Convolve_3x3"),
iaa.Sharpen(alpha=(0.01, 0.99), lightness=(0.5, 2), name="Sharpen"),
iaa.Emboss(alpha=(0.01, 0.99), strength=(0, 2), name="Emboss"),
iaa.EdgeDetect(alpha=(0.01, 0.99), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.01, 0.99), name="DirectedEdgeDetect")
]
augmenters_edges = [iaa.Canny(alpha=(0.01, 0.99), name="Canny")]
augmenters_flip = [
iaa.Fliplr(1.0, name="Fliplr"),
iaa.Flipud(1.0, name="Flipud")
]
augmenters_geometric = [
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=0,
mode="constant",
cval=(0, 255),
name="Affine_order_0_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="constant",
cval=(0, 255),
name="Affine_order_1_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=3,
mode="constant",
cval=(0, 255),
name="Affine_order_3_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="edge",
cval=(0, 255),
name="Affine_order_1_edge"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="constant",
cval=(0, 255),
backend="skimage",
name="Affine_order_1_constant_skimage"),
# TODO AffineCv2
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=1,
mode="constant",
name="PiecewiseAffine_4x4_order_1_constant"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=0,
mode="constant",
name="PiecewiseAffine_4x4_order_0_constant"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=1,
mode="edge",
name="PiecewiseAffine_4x4_order_1_edge"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=8,
nb_cols=8,
order=1,
mode="constant",
name="PiecewiseAffine_8x8_order_1_constant"),
iaa.PerspectiveTransform(scale=(0.01, 0.05),
keep_size=False,
name="PerspectiveTransform"),
iaa.PerspectiveTransform(scale=(0.01, 0.05),
keep_size=True,
name="PerspectiveTransform_keep_size"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=0,
mode="constant",
cval=0,
name="ElasticTransformation_order_0_constant"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="constant",
cval=0,
name="ElasticTransformation_order_1_constant"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="nearest",
cval=0,
name="ElasticTransformation_order_1_nearest"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="reflect",
cval=0,
name="ElasticTransformation_order_1_reflect"),
iaa.Rot90((1, 3), keep_size=False, name="Rot90"),
iaa.Rot90((1, 3), keep_size=True, name="Rot90_keep_size")
]
augmenters_pooling = [
iaa.AveragePooling(kernel_size=(1, 16),
keep_size=False,
name="AveragePooling"),
iaa.AveragePooling(kernel_size=(1, 16),
keep_size=True,
name="AveragePooling_keep_size"),
iaa.MaxPooling(kernel_size=(1, 16), keep_size=False,
name="MaxPooling"),
iaa.MaxPooling(kernel_size=(1, 16),
keep_size=True,
name="MaxPooling_keep_size"),
iaa.MinPooling(kernel_size=(1, 16), keep_size=False,
name="MinPooling"),
iaa.MinPooling(kernel_size=(1, 16),
keep_size=True,
name="MinPooling_keep_size"),
iaa.MedianPooling(kernel_size=(1, 16),
keep_size=False,
name="MedianPooling"),
iaa.MedianPooling(kernel_size=(1, 16),
keep_size=True,
name="MedianPooling_keep_size")
]
augmenters_segmentation = [
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=64,
interpolation="cubic",
name="Superpixels_max_size_64_cubic"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=64,
interpolation="linear",
name="Superpixels_max_size_64_linear"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=128,
interpolation="linear",
name="Superpixels_max_size_128_linear"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=224,
interpolation="linear",
name="Superpixels_max_size_224_linear"),
iaa.UniformVoronoi(n_points=(250, 1000), name="UniformVoronoi"),
iaa.RegularGridVoronoi(n_rows=(16, 31),
n_cols=(16, 31),
name="RegularGridVoronoi"),
iaa.RelativeRegularGridVoronoi(n_rows_frac=(0.07, 0.14),
n_cols_frac=(0.07, 0.14),
name="RelativeRegularGridVoronoi"),
]
augmenters_size = [
iaa.Resize((0.8, 1.2), interpolation="nearest", name="Resize_nearest"),
iaa.Resize((0.8, 1.2), interpolation="linear", name="Resize_linear"),
iaa.Resize((0.8, 1.2), interpolation="cubic", name="Resize_cubic"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
keep_size=False,
name="CropAndPad"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="edge",
pad_cval=(0, 255),
keep_size=False,
name="CropAndPad_edge"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
name="CropAndPad_keep_size"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
keep_size=False,
name="Pad"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="edge",
pad_cval=(0, 255),
keep_size=False,
name="Pad_edge"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
name="Pad_keep_size"),
iaa.Crop(percent=(0.05, 0.2), keep_size=False, name="Crop"),
iaa.Crop(percent=(0.05, 0.2), name="Crop_keep_size"),
iaa.PadToFixedSize(width=width + 10,
height=height + 10,
pad_mode="constant",
pad_cval=(0, 255),
name="PadToFixedSize"),
iaa.CropToFixedSize(width=width - 10,
height=height - 10,
name="CropToFixedSize"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="nearest",
name="KeepSizeByResize_CropToFixedSize_nearest"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="linear",
name="KeepSizeByResize_CropToFixedSize_linear"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="cubic",
name="KeepSizeByResize_CropToFixedSize_cubic"),
]
augmenters_weather = [
iaa.FastSnowyLandscape(lightness_threshold=(100, 255),
lightness_multiplier=(1.0, 4.0),
name="FastSnowyLandscape"),
iaa.Clouds(name="Clouds"),
iaa.Fog(name="Fog"),
iaa.CloudLayer(intensity_mean=(196, 255),
intensity_freq_exponent=(-2.5, -2.0),
intensity_coarse_scale=10,
alpha_min=0,
alpha_multiplier=(0.25, 0.75),
alpha_size_px_max=(2, 8),
alpha_freq_exponent=(-2.5, -2.0),
sparsity=(0.8, 1.0),
density_multiplier=(0.5, 1.0),
name="CloudLayer"),
iaa.Snowflakes(name="Snowflakes"),
iaa.SnowflakesLayer(density=(0.005, 0.075),
density_uniformity=(0.3, 0.9),
flake_size=(0.2, 0.7),
flake_size_uniformity=(0.4, 0.8),
angle=(-30, 30),
speed=(0.007, 0.03),
blur_sigma_fraction=(0.0001, 0.001),
name="SnowflakesLayer")
]
augmenters = (augmenters_meta + augmenters_arithmetic + augmenters_blend +
augmenters_blur + augmenters_color + augmenters_contrast +
augmenters_convolutional + augmenters_edges +
augmenters_flip + augmenters_geometric + augmenters_pooling +
augmenters_segmentation + augmenters_size +
augmenters_weather)
if only_augmenters is not None:
augmenters_reduced = []
for augmenter in augmenters:
if any([
re.search(pattern, augmenter.name)
for pattern in only_augmenters
]):
augmenters_reduced.append(augmenter)
augmenters = augmenters_reduced
return augmenters
def augmentation_of_image(self, test_image, output_path):
self.test_image = test_image
self.output_path = output_path
#define the Augmenters
#properties: A range of values signifies that one of these numbers is randmoly chosen for every augmentation for every batch
# Apply affine transformations to each image.
rotate = iaa.Affine(rotate=(-90, 90))
scale = iaa.Affine(scale={
"x": (0.5, 0.9),
"y": (0.5, 0.9)
})
translation = iaa.Affine(translate_percent={
"x": (-0.15, 0.15),
"y": (-0.15, 0.15)
})
shear = iaa.Affine(shear=(-2, 2))
#plagio parallhlogrammo wihthin a range (-8,8)
zoom = iaa.PerspectiveTransform(
scale=(0.01, 0.15),
keep_size=True) # do not change the output size of the image
h_flip = iaa.Fliplr(1.0)
# flip horizontally all images (100%)
v_flip = iaa.Flipud(1.0)
#flip vertically all images
padding = iaa.KeepSizeByResize(
iaa.CropAndPad(percent=(0.05, 0.25))
) #positive values correspond to padding 5%-25% of the image,but keeping the origial output size of the new image
#More augmentations
blur = iaa.GaussianBlur(
sigma=(0, 1.22)
) # blur images with a sigma 0-2,a number ofthis range is randomly chosen everytime.Low values suggested for this application
contrast = iaa.contrast.LinearContrast((0.75, 1.5))
#change the contrast by a factor of 0.75 and 1.5 sampled randomly per image
contrast_channels = iaa.LinearContrast(
(0.75, 1.5), per_channel=True
) #and for 50% of all images also independently per channel:
sharpen = iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5))
#sharpen with an alpha from 0(no sharpening) - 1(full sharpening) and change the lightness form 0.75 to 1.5
gauss_noise = iaa.AdditiveGaussianNoise(
scale=0.111 * 255, per_channel=True
) #some random gaussian noise might occur in cell images,especially when image quality is poor
laplace_noise = iaa.AdditiveLaplaceNoise(
scale=(0, 0.111 * 255)
) #we choose to be in a small range, as it is logical for training the cell images
#Brightness
brightness = iaa.Multiply(
(0.35, 1.65
)) #change brightness between 35% or 165% of the original image
brightness_channels = iaa.Multiply(
(0.5, 1.5), per_channel=0.75
) # change birghtness for 25% of images.For the remaining 75%, change it, but also channel-wise.
#CHANNELS (RGB)=(Red,Green,Blue)
red = iaa.WithChannels(0, iaa.Add(
(10,
100))) #increase each Red-pixels value within the range 10-100
red_rot = iaa.WithChannels(0, iaa.Affine(
rotate=(0, 45))) #rotate each image's red channel by 0-45 degrees
green = iaa.WithChannels(1, iaa.Add(
(10,
100))) #increase each Green-pixels value within the range 10-100
green_rot = iaa.WithChannels(1, iaa.Affine(
rotate=(0,
45))) #rotate each image's green channel by 0-45 degrees
blue = iaa.WithChannels(2, iaa.Add(
(10,
100))) #increase each Blue-pixels value within the range 10-100
blue_rot = iaa.WithChannels(2, iaa.Affine(
rotate=(0, 45))) #rotate each image's blue channel by 0-45 degrees
#colors
channel_shuffle = iaa.ChannelShuffle(1.0)
#shuffle all images of the batch
grayscale = iaa.Grayscale(1.0)
hue_n_saturation = iaa.MultiplyHueAndSaturation(
(0.5, 1.5), per_channel=True
) #change hue and saturation with this range of values for different values
add_hue_saturation = iaa.AddToHueAndSaturation(
(-50, 50),
per_channel=True) #add more hue and saturation to its pixels
#Quantize colors using k-Means clustering
kmeans_color = iaa.KMeansColorQuantization(
n_colors=(4, 16)
) #quantizes to k means 4 to 16 colors (randomly chosen). Quantizes colors up to 16 colors
#Alpha Blending
blend = iaa.AlphaElementwise((0, 1.0), iaa.Grayscale((0, 1.0)))
#blend depending on which value is greater
#Contrast augmentors
clahe = iaa.CLAHE(tile_grid_size_px=((3, 21), [
0, 2, 3, 4, 5, 6, 7
])) #create a clahe contrast augmentor H=(3,21) and W=(0,7)
histogram = iaa.HistogramEqualization(
) #performs histogram equalization
#Augmentation list of metadata augmentors
OneofRed = iaa.OneOf([red])
OneofGreen = iaa.OneOf([green])
OneofBlue = iaa.OneOf([blue])
contrast_n_shit = iaa.OneOf(
[contrast, brightness, brightness_channels])
SomeAug = iaa.SomeOf(
2, [rotate, scale, translation, shear, h_flip, v_flip],
random_order=True)
SomeClahe = iaa.SomeOf(
2, [
clahe,
iaa.CLAHE(clip_limit=(1, 10)),
iaa.CLAHE(tile_grid_size_px=(3, 21)),
iaa.GammaContrast((0.5, 2.0)),
iaa.AllChannelsCLAHE(),
iaa.AllChannelsCLAHE(clip_limit=(1, 10), per_channel=True)
],
random_order=True) #Random selection from clahe augmentors
edgedetection = iaa.OneOf([
iaa.EdgeDetect(alpha=(0, 0.7)),
iaa.DirectedEdgeDetect(alpha=(0, 0.7), direction=(0.0, 1.0))
])
# Search in some images either for all edges or for directed edges.These edges are then marked in a black and white image and overlayed with the original image using an alpha of 0 to 0.7.
canny_filter = iaa.OneOf([
iaa.Canny(),
iaa.Canny(alpha=(0.5, 1.0), sobel_kernel_size=[3, 7])
])
#choose one of the 2 canny filter options
OneofNoise = iaa.OneOf([blur, gauss_noise, laplace_noise])
Color_1 = iaa.OneOf([
channel_shuffle, grayscale, hue_n_saturation, add_hue_saturation,
kmeans_color
])
Color_2 = iaa.OneOf([
channel_shuffle, grayscale, hue_n_saturation, add_hue_saturation,
kmeans_color
])
Flip = iaa.OneOf([histogram, v_flip, h_flip])
#Define the augmentors used in the DA
Augmentors = [
SomeAug, SomeClahe, SomeClahe, edgedetection, sharpen,
canny_filter, OneofRed, OneofGreen, OneofBlue, OneofNoise, Color_1,
Color_2, Flip, contrast_n_shit
]
for i in range(0, 14):
img = cv2.imread(test_image) #read you image
images = np.array(
[img for _ in range(14)], dtype=np.uint8
) # 12 is the size of the array that will hold 8 different images
images_aug = Augmentors[i].augment_images(
images
) #alternate between the different augmentors for a test image
cv2.imwrite(
os.path.join(output_path,
test_image + "new" + str(i) + '.jpg'),
images_aug[i]) #write all changed images
iaa.ContrastNormalization((0.9, 1.1)),
# Add gaussian noise.
# For 50% of all images, we sample the noise once per pixel.
# For the other 50% of all images, we sample the noise per pixel AND
# channel. This can change the color (not only brightness) of the
# pixels.
iaa.AdditiveGaussianNoise(
loc=0, scale=(0.0, 0.05 * 255), per_channel=0.5),
# Make some images brighter and some darker.
# In 20% of all cases, we sample the multiplier once per channel,
# which can end up changing the color of the images.
iaa.Multiply((0.9, 1.1), per_channel=0.2),
# Apply affine transformations to each image.
# Scale/zoom them, translate/move them, rotate them and shear them.
iaa.KeepSizeByResize(
iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
rotate=(-10, 10),
shear=(-8, 8),
mode="symmetric"))
#translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)},
],
random_order=True) # apply augmenters in random order
for j in range(4):
images_aug = seq.augment_images(imgs)
for i in range(images_aug.shape[0]):
imageio.imwrite('G:\\final_dataset\\paper\\{}_{}.png'.format(j, i),
images_aug[i])
seq_1 = iaa.Sequential(
[
# apply only 2 of the following
iaa.SomeOf(3, [
sometimes(iaa.Fliplr(0.99)),
sometimes(iaa.Flipud(0.99)),
sometimes(iaa.Affine(scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}, order=1, backend="cv2")),
sometimes(iaa.Affine(translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)}, order=1, backend="cv2")),
sometimes(iaa.Affine(rotate=(-90, 90), order=1, backend="cv2")),
sometimes(iaa.Affine(shear=(-25, 25), order=1, backend="cv2")),
sometimes(iaa.ElasticTransformation(alpha=50.0, sigma=5.0)),
# radomly crop some part of image to add more BG class in data
sometimes(iaa.CropToFixedSize(width=512, height=512, position='uniform')),
iaa.OneOf([
sometimes(iaa.KeepSizeByResize(
iaa.Crop(percent=(0.05, 0.25), keep_size=False),
interpolation='linear')),
sometimes(iaa.KeepSizeByResize(
iaa.CropAndPad(percent=(0.05, 0.25), pad_mode=["constant", "edge"], pad_cval=(0, 255)),
interpolation="linear"))
]),
], random_order=True),
], random_order=True)
''' Noisy Data Aug'''
seq_2 = iaa.Sequential(
[
iaa.OneOf(
[
# Blur each image using a median over neihbourhoods that have a random size between 3x3 and 7x7
sometimes(iaa.MedianBlur(k=(3, 7))),
# blur images using gaussian kernels with random value (sigma) from the interval [a, b]
# iaa.Crop(px=(0, 70)),
# iaa.Affine(rotate=(-25, 25)),
# iaa.Affine(translate_percent={"x": (-0.2, 0.2), "y": (-0.2, 0.2)})
# ],random_order=True),
], random_order=True)
''' SSD Upscale Crop'''
# randomly choose positions in image
positions = ['center','left-top', 'left-center', 'left-bottom', 'center-top',
'center-bottom', 'right-top', 'right-center', 'right-bottom']
SSD = iaa.Sequential([
iaa.KeepSizeByResize(iaa.CropToFixedSize(width=470, height=370, position=positions[0]), interpolation="linear")
], random_order=True)
#%
#for i in range(10):
image_aug, bbs_aug = SSD.augment(image=img, bounding_boxes=bbs)
# disregard bounding boxes which have fallen out of image pane
bbs_aug = bbs_aug.remove_out_of_image()
# clip bounding boxes which are partially outside of image pane
bbs_aug = bbs_aug.clip_out_of_image()
#ia.imshow(bbs_aug.draw_on_image(image_aug, size=5))
'''
Now updata the dictionary wiht new augmented values